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Dr Jose (Pepe) Romeo, PhD

 

Dr Jose (Pepe) Romeo is a researcher in Statistics specialising in Survival Analysis. He holds a Statistical Engineering degree from the University of Santiago, Chile, and a PhD degree in Statistics from the University of Sao Paulo, Brazil. He was a postdoctoral research fellow in the Department of Statistics at the University of Auckland carrying out research on Bayesian Survival Analysis. He has taught many courses related to Statistics at undergraduate and postgraduate level across different universities. His experience as a consultant involved work in state-owned companies and private organizations such as public ministries, banks and health providers. He has interest in applied statistical models for decision making, using methods such as Bayesian Inference, Generalized Linear Models, Time Series and Multivariate Data Analysis.

Research Interests:
Statistical Modelling | Biostatistics | Bayesian Inference | Regression models | Survival Analysis | Copula and Frailty models | Multivariate methods

Email: j.romeo@massey.ac.nz 


journal articles

Romeo, J.S., Meyer, R. and Gallardo, D.I. (2017). Bayesian bivariate survival analysis using the power variance function copula. Lifetime Data Analysis, doi:10.1007/s10985-017-9396-1

Gallardo, D.I., Romeo, J.S. and Meyer, R. (2017). A simplified estimation procedure based on the EM algorithm for the power series cure rate model. Communications in Statistics - Simulation and Computation, doi:10.1080/03610918.2016.1202276.

Poshdar, M., Gonzalez, V.A., Raftery, G.M., Orozco, F., Romeo, J.S. and Forcael, E. (2016). A probabilistic-based method to determine optimum size of project buffer in construction schedules. Journal of Construction Engineering and Management, 142(10), 04016046.

Meyer, R. and Romeo, J.S. (2015). Bayesian semiparametric analysis of recurrent failure time data using copulas. Biometrical Journal, 57, 982-1001.

Romeo, J.S. and Meyer, R. (2015). Bayesian approach for modelling bivariate survival data through the PVF copula. In Friedl, H. and Wagner, H. (Eds.), Proceedings of the 30th International Workshop on Statistical Modelling, vol. 2. Linz, Austria, 239-242.

Reyes-Lopez, F.E., Romeo, J.S., Vallejos-Vidal, E., Reyes-Cerpa, S., Sandino, A.M., Tort, L., Mackenzie, S. and Imarai, M. (2015). Differential immune gene expression profiles in susceptible and resistant full-sibling families of Atlantic salmon (Salmo salar) challenged with infectious pancreatic necrosis virus (IPNV). Developmental & Comparative Immunology, 53, 210-221.

Romeo, J.S., Meyer, R. and Reyes-Lopez, F. (2014). Hierarchical failure time regression using mixtures for classification of the immune response of Atlantic salmon. Journal of Agricultural, Biological, and Environmental Statistics, 19(4), 501-521.

Roman, S.T., Romeo, J.S. and Salinas, V.H. (2014). Bayesian estimation of the limiting availability in the presence of right-censored data. METRON, 72, 247-267.

Bazan, J.L., Romeo, J.S. and Rodrigues, J. (2014). Bayesian skew-probit regression for binary response data. Brazilian Journal of Probability and Statistics, 28, 467-482.

Torres-Aviles, F., Romeo, J.S. and Lopez-Kleine, L. (2014). Data mining and influential analysis of gene expression data for plant resistance genes identification in tomato (Solanum lycopersicum). Electronic Journal of Biotechnology, 17, 79-82.

Lopez-Kleine, L., Romeo, J.S. and Torres-Aviles, F. (2013). Gene functional prediction using clustering methods for the analysis of tomato microarray data. In Mohamad, M.S., Nanni, L., Rocha, M.P. and Fdez-Riverola, F. (Eds.), 7th International Conference on Practical Applications of Computational Biology & Bioinformatics, Advances in Intelligent Systems and Computing, vol. 222. Springer International Publishing, Switzerland, 1-6.

Romeo, J.S., Torres-Aviles, F. and Lopez-Kleine, L. (2013). Detection of influent virulence and resistance genes in microarray data through quasi likelihood modeling. Molecular Genetics and Genomics, 288, 49-61.

Romeo, J.S., Tanaka, N.I., Pedroso-de-Lima, A.C. and Salinas-Torres, V.H. (2013). Large sample properties for a class of copulas in bivariate survival analysis. Metrika, 76, 997-1015.

Salinas, V.H., Romeo, J.S. and Pena, J.A. (2010). On Bayesian estimation of a survival curve: comparative study and examples. Computational Statistics, 25, 375-389.

Diaz-Ledezma, C., Urrutia, J., Romeo, J.S., Chelen, A., Gonzalez-Wilhelm, L. and Lavarello, C. (2009). Factors associated with variability in length of sick leave because of acute low back pain in Chile. The Spine Journal, 9, 1010-1015.

Romeo, J.S., Tanaka, N.I. and Pedroso-de-Lima, A.C. (2006). Bivariate survival modeling: A Bayesian approach based on copulas. Lifetime Data Analysis, 12, 205-222.